The Evolution of E-commerce: How Artificial Intelligence is Reshaping Online Retail

Abstract

Due to the fast-growing e-commerce, the consumer behavior has changed, and business opportunities have been offered to personalize the experience and streamline operations. Recommendation systems, chatbots, predictive analytics, and computer vision are all examples of Artificial Intelligence (AI) technologies that are transforming online retail in a very critical way. This paper explores how AI can be applied to online shopping using actual online shopping data to conduct experimental research on how AI-based recommendation systems can change consumer behavior and online sales performance. An international e-commerce store consisting of more than 500,000 transactions in a Kaggle dataset were analyzed with the help of collaborative filtering and content-based AI recommendation algorithms. There were comparative experiments with a baseline of a non-personalized system of recommendations and an AI-enhanced one. The most important performance indicators were measured including the click-through rate (CTR), conversion rate (CR), and average order value (AOV). The results obtained showed that AI-based suggestions enhanced CTR (38), CR (24), and AOV (17) in comparison to baseline procedures. These findings show the real effects of AI technologies in enhancing user experience and contributing revenue to an online retail setup. In the end of the study, the methodological implications, limitations and future discussions of incorporating advanced AI methods in the e-commerce ecosystems are discussed